Data Preparation for Generative AI Projects

Clean and structure data for high-quality GenAI input.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course provides a comprehensive guide to preparing data specifically for Generative AI applications. Participants will learn best practices for cleaning, structuring, and transforming data to ensure that AI models receive reliable and high-quality inputs. Emphasis is placed on techniques that enhance the relevance and accuracy of generated outputs, as well as on maintaining data integrity throughout the preparation process.

Learning Objectives

Participants will be able to:

• Understand the unique data requirements for GenAI models

• Apply data cleaning methods to remove noise and inconsistencies

• Structure and format data to optimize AI training and inference

• Address data imbalances and biases before model consumption

• Use tools and workflows for efficient data preprocessing

Target Audience

• Data engineers and data scientists

• AI/ML developers

• Data analysts and BI professionals

• AI project managers and technical leads

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Instructor-led sessions with demonstrations

• Hands-on data cleaning and preprocessing exercises

• Group workshops on real-world GenAI datasets

• Discussions on data challenges and solutions

Materials Provided

• Data preprocessing toolkits and scripts

• Sample datasets for practice

• Checklists for data quality assurance

• Guidelines on ethical data handling

Outcomes

• Ability to prepare clean, structured data tailored for GenAI

• Improved model accuracy and reliability through quality inputs

• Enhanced awareness of data ethics in preparation workflows

• Practical skills in using preprocessing tools and techniques

Outline / Content

Day 1: Introduction to Data Needs for GenAI

• Understanding GenAI data inputs and their impact on outputs

• Common data challenges in AI projects

Day 2: Data Cleaning Techniques

• Removing duplicates, errors, and inconsistencies

• Handling missing values and outliers

Day 3: Structuring and Transforming Data

• Formatting data for different GenAI models

• Feature engineering and data augmentation basics

Day 4: Ethical Considerations and Quality Assurance

• Identifying and mitigating bias in data

• Establishing data quality standards and validation processes